1,089 research outputs found
Variability analysis of engine idle vibration
Vibration in motor vehicles is largely influenced by the engine and thus has become the focus of much automotive testing. Engine idle vibration is focused on since deviations in the vibration signature are prevalent at this operating condition. The objective of this thesis was to derive a best-practice method for the analysis of engine idle vibration. Variability of the engine vibration signatures was calculated through the implementation of multiple analysis techniques. These methods included: angle domain analysis, the fast Fourier transform, the discrete cosine transform, the moving average model, and the auto-regressive moving average model. Also included in the investigation were examinations of data normalization, detrending, and filtration. The results of the analyses were then evaluated with reference to the correlation between similar engines and the identification of outliers. It was found that the fast Fourier transform analysis technique provided the best overall results. The moving average model and the auto-regressive moving average models were also identified as methods that have great potential in vibration analysis but are limited by their computational intensity
Microprocessor Implementation of Autoregressive Analysis of Process Sensor Signals
Automated signal analysis can help for effective system surveillance and also to analyze the dynamic behavior of the system such as impulse response, step response etc. Autoregressive analysis is a parametric technique widely used for system surveillance and diagnosis. The main aim objective of this research work is to develop an embedded system for autoregressive analysis of sensor signals in an online fashion for monitoring system parameters. This thesis presents the algorithm, data representation and performance of the optimized microprocessor implementation of autoregressive analysis.
In this work an autoregressive (AR) model is generated as a solution to a linear system of equations called Yule-Walker linear equations. The generated model is then implemented on Motorola PowerPC MPC555 processor. The embedded software for autoregressive analysis is written in the C programming language using fixed point arithmetic. It includes estimation of the autoregressive parameters, estimation of the noise variance recursively using the AR parameters, determination of the optimal model order and the model validation
A Summary of Research Projects Sponsored by the Office of Naval Research. Report for the Period 1 October 1979 to 30 September 1980
Office of Naval Researc
UAS Model Identification and Simulation to Support In-Flight Testing of Discrete Adaptive Fault-Tolerant Control Laws
In mission-critical applications of unmanned and autonomous aerial systems(UAS), it is of significant importance to develop robust strategies for fault-tolerant systems that can countermeasure system degradation and consequently support the integration into the National Airspace (NAS). This thesis research illustrates the results of systems identification that is performed using DATCOM followed by the flight test data. This data is acquired from conducting an intensive flight testings program of a fixed-wing UAS to determine the state-space model of the aircraft. A discrete state-space system is reconstructed from these models to derive Auto-Regressive Moving-Average (ARMA) models used to design a Discrete Direct and Indirect Model Reference Adaptive Control. Description of the UAS, sub-systems, and integration is presented in this thesis along with analysis of results from numerical simulation to support the design, development, and validation of adaptive control laws for fault tolerance. A set of performance metrics are defined to perform the analysis in terms of control effort, tracking performance, and reconfiguration of control laws under commonly occurring failures such as partial control surface damage, pilot-induced oscillations, and uncertain ice accretion
Soft sensors in automotive applications
2017 - 2018In this work, design and validation techniques of two soft sensors for the estimation of the motorcycle vertical
dynamic have been proposed. The aim of this work is to develop soft sensors able to predict the rear and front
stroke of a motorcycle suspension. This kind of information are typically used in the control loop of semi‐active
or active suspension systems. Replacing the hard sensor with a soft sensor, enable to reduce cost and improve
reliability of the system. An analysis of the motorcycle physical model has been carried out to analyze the
correlation existing among motorcycle vertical dynamic quantities in order to determine which of them are
necessary for the development of a suspension stroke soft sensor. More in details, a first soft sensor for the rear
stroke has been developed using a Nonlinear Auto‐Regressive with eXogenous inputs (NARX) neural network. A
second soft sensor for the front suspension stroke velocity has been designed using two different techniques
based respectively on Digital filtering and NARX neural network. As an example of application, an Instrument
Fault Detection (IFD) scheme, based on the rear stroke soft sensor, has been shown. Experimental results have
demonstrated the good reliability and promptness of the scheme in detecting different typologies of faults as
losing calibration faults, hold‐faults, and open/short circuit faults thanks to the soft sensor developed. Finally,
the scheme has been successfully implemented and tested on an ARM microcontroller, to confirm the feasibility
of a real‐time implementation on actual processing units used in such context. [edited by Author]XXX cicl
Fast recursive filters for simulating nonlinear dynamic systems
A fast and accurate computational scheme for simulating nonlinear dynamic
systems is presented. The scheme assumes that the system can be represented by
a combination of components of only two different types: first-order low-pass
filters and static nonlinearities. The parameters of these filters and
nonlinearities may depend on system variables, and the topology of the system
may be complex, including feedback. Several examples taken from neuroscience
are given: phototransduction, photopigment bleaching, and spike generation
according to the Hodgkin-Huxley equations. The scheme uses two slightly
different forms of autoregressive filters, with an implicit delay of zero for
feedforward control and an implicit delay of half a sample distance for
feedback control. On a fairly complex model of the macaque retinal horizontal
cell it computes, for a given level of accuracy, 1-2 orders of magnitude faster
than 4th-order Runge-Kutta. The computational scheme has minimal memory
requirements, and is also suited for computation on a stream processor, such as
a GPU (Graphical Processing Unit).Comment: 20 pages, 8 figures, 1 table. A comparison with 4th-order Runge-Kutta
integration shows that the new algorithm is 1-2 orders of magnitude faster.
The paper is in press now at Neural Computatio
Model based control of exhaust gas recirculation valves and estimation of spring torque
Exhaust Gas Recirculation (EGR) valves have been used in diesel engine operation to reduce NOx emissions. In EGR valve operation, the amount of exhaust gas re-circulating back into the intake manifold is controlled through the open position of the valve plate to keep the combustion temperature lower for NOx emission reduction. Most of the control methods do not provide sufficient control accuracy on the valve position and the response time. Here, the model of a motor driven EGR valve is first identified through experiments and then the Generalized Predictive Control (GPC) method is applied to control the plate position of the valve. At the next step, to have a faster and more accurate control of the valve the torque generated by the spring connected inside the valve is estimated. The spring torque is considered as an external disturbance torque and three filters: Kalman, H∞ and H∞ Gaussian Filters are used to estimated this torque
Yaw rate control of an air bearing vehicle
The results of a 6 week project which focused on the problem of controlling the yaw (rotational) rate the air bearing vehicle used on NASA's flat floor facility are summarized. Contained within is a listing of the equipment available for task completion and an evaluation of the suitability of this equipment. The identification (modeling) process of the air bearing vehicle is detailed as well as the subsequent closed-loop control strategy. The effectiveness of the solution is discussed and further recommendations are included
On the Detection of Cyber-Attacks in the Communication Network of IEC 61850 Electrical Substations
The availability of the data within the network communication remains one of the most critical requirement when compared to integrity and confidentiality. Several threats such as Denial of Service (DoS) or flooding attacks caused by Generic Object Oriented Substation Event (GOOSE) poisoning attacks, for instance, might hinder the availability of the communication within IEC 61850 substations.
To tackle such threats, a novel method for the Early Detection of Attacks for the GOOSE Network Traffic (EDA4GNeT) is developed in the present work.
Few of previously available intrusion detection systems take into account the specific features of IEC 61850 substations and offer a good trade-off between the detection performance and the detection time. Moreover, to the best of our knowledge, none of the existing works proposes an early anomaly detection method of GOOSE attacks in the network traffic of IEC 61850 substations that account for the specific characteristics of the network data in electrical substations.
The EDA4GNeT method considers the dynamic behavior of network traffic in electrical substations. The mathematical modeling of the GOOSE network traffic first enables the development of the proposed method for anomaly detection. In addition, the developed model can also support the management of the network architecture in IEC 61850 substations based on appropriate performance studies. To test the novel anomaly detection method and compare the obtained results with available techniques, two use cases are used
- …